ReviewMateria socio-medica2024
Optimization of Radiology Diagnostic Services for Patients with Stroke in Multidisciplinary Hospitals.
Review in Materia socio-medica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Medical students' perception of AI's role in radiology before and after an AI-focused educational panel: a paired pre-post design.BMC medical education · 2025Article
- Optimizing MRI Scheduling in High-Complexity Hospitals: A Digital Twin and Reinforcement Learning Approach.Bioengineering (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Effective radiology diagnostic services are crucial for the timely and precise diagnosis and treatment of stroke, a medical emergency, in multidisciplinary hospitals. However, the efficiency of these services might be impeded by various logistical and operational challenges present in a multidisciplinary hospital setup. Objective: This review endeavours to explore the ways for optimizing stroke management in multi-disciplinary hospitals, delving into its benefits, current challenges, and future prospects. Methods: Electronic databases, namely PubMed, Scopus, and Web of Science, were utilized for this review. Studies that focus on the organizational and functional aspects of radiology diagnostic services in multidisciplinary hospitals for stroke patients were analysed. Results: This review delves into a variety of strategies that could be harnessed to enhance radiology diagnostic services, thereby better-serving stroke patients in multidisciplinary hospital settings. It sheds light on the current hurdles in the optimization of stroke management, discussing them in detail. This article also explores the application and significance of Process Mapping in streamlining workflow for stroke management in hospitals, providing insights into its benefits, challenges, and future implications. Furthermore, the potential of Artificial Intelligence (AI) and Machine Learning (ML) in refining stroke management processes is also analysed and discussed. Conclusion: The quest for optimizing the organization of radiology diagnostic services in multidisciplinary hospitals unveils a multi-pronged pathway. It beckons a harmonious blend of technological innovation, operational finesse, and multidisciplinary camaraderie. stepwise implementation of the identified optimization strategies, coupled with a continual assessment of their impact on patient care and operational efficiency, is recommended.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.